{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "source": [
    "s = \"Hello,Python!\"\n",
    "print(len(s))"
   ],
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "13\n"
     ]
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "source": [
    "i = 1\n",
    "sum = 0\n",
    "while i <= 100:\n",
    "    sum += i\n",
    "    i += 1\n",
    "print(sum)"
   ],
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "5050\n"
     ]
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "source": [
    "fruit_list = ['apple', 'pair', 'banana', 'orange', 'watermelon']\n",
    "[print(i) for i in fruit_list]"
   ],
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "apple\n",
      "pair\n",
      "banana\n",
      "orange\n",
      "watermelon\n"
     ]
    },
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "[None, None, None, None, None]"
      ]
     },
     "metadata": {},
     "execution_count": 5
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "source": [
    "import os\n",
    "class Test:\n",
    "\n",
    "    def __init__(self):\n",
    "        self.text = \"\"\n",
    "        self.getString()\n",
    "\n",
    "    def getString(self):\n",
    "        self.text = input(\"input: \")\n",
    "\n",
    "    def printString(self):\n",
    "        print(self.text.upper())\n",
    "\n",
    "t = Test()\n",
    "t.printString()"
   ],
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "TEST\n"
     ]
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "source": [
    "import numpy as np\n",
    "A = np.array([[1, -2, 4], [1, -1, 1], [1, 0, 0], [1, 1, 1], [1, 2, 4]])\n",
    "B = np.array([[1, 2, 3], [1, 2, 3], [1, 2, 3], [1, 2, 3], [1, 2, 3]])\n",
    "\n",
    "print(A)\n",
    "print(B)"
   ],
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "[[ 1 -2  4]\n",
      " [ 1 -1  1]\n",
      " [ 1  0  0]\n",
      " [ 1  1  1]\n",
      " [ 1  2  4]]\n",
      "[[1 2 3]\n",
      " [1 2 3]\n",
      " [1 2 3]\n",
      " [1 2 3]\n",
      " [1 2 3]]\n"
     ]
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "source": [
    "print(A[1, :])\n",
    "print(B[:, 2])"
   ],
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "[ 1 -1  1]\n",
      "[3 3 3 3 3]\n"
     ]
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "source": [
    "print(A + B)\n",
    "print(A - B)"
   ],
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "[[2 0 7]\n",
      " [2 1 4]\n",
      " [2 2 3]\n",
      " [2 3 4]\n",
      " [2 4 7]]\n",
      "[[ 0 -4  1]\n",
      " [ 0 -3 -2]\n",
      " [ 0 -2 -3]\n",
      " [ 0 -1 -2]\n",
      " [ 0  0  1]]\n"
     ]
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "source": [
    "print(np.hstack((A, B)))"
   ],
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "[[ 1 -2  4  1  2  3]\n",
      " [ 1 -1  1  1  2  3]\n",
      " [ 1  0  0  1  2  3]\n",
      " [ 1  1  1  1  2  3]\n",
      " [ 1  2  4  1  2  3]]\n"
     ]
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "source": [
    "print(np.dot(A.T, B))"
   ],
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "[[ 5 10 15]\n",
      " [ 0  0  0]\n",
      " [10 20 30]]\n"
     ]
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "source": [
    "import matplotlib.pyplot as plt\n",
    "t = np.linspace(0, np.pi * 2, 500)\n",
    "plt.figure(figsize=(6, 6))\n",
    "plt.plot(np.cos(t), np.sin(t))\n",
    "plt.show()"
   ],
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "source": [
    "t = np.linspace(0, np.pi * 2, 500)\n",
    "h_x = np.linspace(-1, 1, 500)\n",
    "h_y = np.zeros(500)\n",
    "\n",
    "v_x = np.zeros(500)\n",
    "v_y = np.linspace(-1, 1, 500)\n",
    "\n",
    "plt.figure(figsize=(6, 6))\n",
    "plt.plot(np.cos(t), np.sin(t), h_x, h_y, v_x, v_y)\n",
    "plt.show()"
   ],
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "metadata": {}
  }
 ],
 "metadata": {
  "orig_nbformat": 4,
  "kernelspec": {
   "name": "python3",
   "display_name": "Python 3",
   "language": "python"
  },
  "language_info": {
   "name": "python",
   "version": "3.7.4",
   "mimetype": "text/x-python",
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "pygments_lexer": "ipython3",
   "nbconvert_exporter": "python",
   "file_extension": ".py"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}